Heating, ventilating and air conditioning (HVAC) equipment is expensive to install and maintain. Therefore, fault detection and diagnosis (FDD) can reduce costs. Many prior art FDD techniques for HVAC equipment are based on analyzing the equipment after the equipment reaches a steady-state operating condition.
One ruled based expert system senses operating features, such as condensing temperature, evaporating temperature, and condenser inlet temperature. The features are then used to detect and diagnose faults by means of decision rules, Stallard, L. A., “Model based expert system for failure detection and identification of household refrigerators,” Master's thesis, School of Mechanical Engineering, Purdue University, West Lafayette, Ind., 1989.
U.S. Pat. No. 6,223,544 issued to Seem on May 1, 2001, “Integrated control and fault detection of HVAC equipment,” describes a finite state machine controller for HVAC equipment. That method uses data regarding the equipment performance in a current state, and upon a transition, determines whether a fault condition exists. The fault detection can be based on saturation of the system control, or on a comparison of actual performance to a model of the equipment. As a consequence, the controller does not have to detect steady-state operation to perform fault detection.
Some methods use physical models, such as the ACMODEL, Rossi, T., and Braun, J., “A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners,” International Journal of HVAC Research 3, 19-37, 1997. That method has had limited success. Their accuracy is low. They require detailed mechanical descriptions of the equipment. Their method is not probabilistic. Even if they could produce an accurate estimate of an expected operating state as a function of driving conditions, such estimates are deterministic, and do not show what kind of deviations from an expected state are acceptable and what are not.
For this reason, FDD has turned towards statistical machine learning (SML) models, also known as “black-box models.” Such models ignore completely the physical nature of the relationships between driving conditions, device construction, and normal operating states. Instead, those models ‘learn’ the relationships from data, see Braun, J., and Li, H. “Automated fault detection and diagnosis of rooftop air conditioners for California,” Final Report 2.1.6a,b, Purdue University, Mechanical Engineering Dept., 2003, and Zogg, D., “Fault diagnosis for heat pump systems,” PhD thesis, Swiss Federal Institute of Technology, Zurich, 2002. In those approaches, fault detection and fault diagnosis are addressed as two separate problems that are solved in sequence by means of two different classes of SML methods.